The present invention relates to speech recognition, and more particularly relates to a scoring unit for a speech recognition system that is implemented in hardware.
Speech recognition tools translate human speech data into searchable text. Whether running on a desktop personal computer (PC) or an enterprise server farm, today's state-of-the-art speech recognizers exist as complex software running on conventional computers. This is profoundly limiting for applications that require extreme recognition speed. Today's most sophisticated recognizers fully occupy the computational resources of a high-end server to deliver results at, or near, real-time speed where each hour of audio input requires roughly one hour of computation for recognition. Applications range from homeland security, such as searching through large streams of audio intercepts for threats to national security, to video indexing, such as automatically creating a computer-readable text transcription from an audio component or soundtrack of a recorded video.
The high level architecture of a modern, state-of-the-art speech recognition system 10 is illustrated in
Next, the acoustic scoring stage 14 receives the feature vector 20 for the speech heard in one input frame, and matches the feature vector 20 against a large library of stored atomic sounds. These atomic sounds are obtained from training over a very large number of speakers, all speaking from a target vocabulary. In the earliest recognizers, these atomic units of speech were phonemes, or phones, where phones are the smallest units of sound that distinguish meaning in different words. There are approximately 50 such phones in the English language, corresponding roughly to the familiar consonant and vowel sounds. For example, “five” has three phones: /f/ /i/ /v/, and “nine” also has three phones: /n/ /i/ /n/. Modern recognizers improve on this idea by modeling phones in context, as illustrated in
As graphically illustrated in
where n(s) is the number of Gaussians in the mixture, ws,i is a weight of the i-th Gaussian for senone s, |Λs,i| is the determinant of covariance matrix Λs,i for the i-th Gaussian for senone s, σs,i,j2 is the variance for the j-th dimension of d-dimensional density for the i-th Gaussian for senone s, xj is the j-th element of d-dimensional feature vector X, and μs,i,j is the j-th element of a d-dimensional mean for the i-th Gaussian for senone s.
In conventional usage, the logarithm (log) of the GMM probability is used for subsequent computational convenience. This log(probability) is calculated for each senone and delivered to the following backend search stage 16. A complex acoustic model can easily have 10,000 senones, each modeled with 64 Gaussians, in a space dimension between 30 and 50. The output of the acoustic scoring stage 14 is a vector of scores—10,000 log(probability) numbers, in this case—one per senone. Note that a new feature vector 20 is input to the acoustic scoring stage 14 for each frame of sampled speech. In response, the acoustic scoring stage 14 outputs a vector of scores including one score per senone for each frame of sampled speech based on the corresponding feature vector 20.
Returning to
A complex acoustic model comprises: a high-dimensional feature vector delivered every few milliseconds; a large library of stored atomic sounds called senones; and for each senone, a numerical GMM comprising a large set of high-dimensional Gaussian densities. For each frame of sampled speech (i.e., every few milliseconds), the acoustic scoring stage 14 is required to calculate a likelihood score—a log(probability)—for each senone in the acoustic model and deliver the scores for the senones to the backend search stage 16 for subsequent recognition of word fragments (phones), words, and word sequences (from a language model). Thus, one issue with conventional implementations of the speech recognition system 10 is that the acoustic scoring stage 14 is a bottleneck for applications that require extreme speed. As such, there is a need for a high-speed acoustic scoring stage for a speech recognition system.
The present invention provides a hardware implemented acoustic scoring unit for a speech recognition system and a method of operation thereof. In general, the acoustic scoring unit includes acoustic scoring logic and high score ciphone identification logic. Rather than scoring all senones in an acoustic model used for the speech recognition system, the acoustic scoring logic first scores a set of context independent phones, or ciphones, based on acoustic features for one frame of sampled speech. The acoustic scoring logic then scores a number of senones associated with a number (N) of highest scored ciphones from the set of ciphones. In one embodiment, the number (N) is three. While the acoustic scoring logic scores the senones associated with the N highest scored ciphones, the high score ciphone identification logic operates in parallel with the acoustic scoring unit to identify one or more additional ciphones from the set of ciphones, other than the N highest scored ciphones, that have scores greater than a threshold. Once the acoustic scoring unit finishes scoring the senones for the N highest scored ciphones, the acoustic scoring unit then proceeds to score senones associated with the one or more additional ciphones identified by the high scoring ciphone identification logic.
Those skilled in the art will appreciate the scope of the present invention and realize additional aspects thereof after reading the following detailed description of the preferred embodiments in association with the accompanying drawing figures.
The accompanying drawing figures incorporated in and forming a part of this specification illustrate several aspects of the invention, and together with the description serve to explain the principles of the invention.
The embodiments set forth below represent the necessary information to enable those skilled in the art to practice the invention and illustrate the best mode of practicing the invention. Upon reading the following description in light of the accompanying drawing figures, those skilled in the art will understand the concepts of the invention and will recognize applications of these concepts not particularly addressed herein. It should be understood that these concepts and applications fall within the scope of the disclosure and the accompanying claims.
The present invention provides a hardware acoustic scoring unit for a speech recognition system and a method of operation thereof. In general, the acoustic scoring unit includes acoustic scoring logic and high score ciphone identification logic. Rather than scoring all senones in an acoustic model used for the speech recognition system, the acoustic scoring logic first scores a set of context independent phones, or ciphones, based on acoustic features for one frame of sampled speech. The acoustic scoring logic then scores a number of senones, or atomic sounds, associated with a number (N) of highest scored ciphones from the set of ciphones. In one embodiment, the number (N) is three. While the acoustic scoring logic scores the senones associated with the N highest scored ciphones, the high score ciphone identification logic operates in parallel with the acoustic scoring unit to identify one or more additional ciphones from the set of ciphones, other than the N highest scored ciphones, that have scores greater than a threshold. Once the acoustic scoring unit finishes scoring the senones for the N highest scored ciphones, the acoustic scoring unit then proceeds to score senones associated with the one or more additional ciphones identified by the high scoring ciphone identification logic.
In the preferred embodiment, by operating the high score ciphone identification logic in parallel with the acoustic scoring logic, the acoustic scoring logic is generally enabled to substantially continuously score either ciphones or senones until acoustic scoring for the frame is complete. Further, by scoring only those senones associated with the N highest scored ciphones and the one or more additional ciphones having scores greater than the predetermined threshold, rather than scoring all senones, and by enabling the acoustic scoring logic to substantially continuously score either ciphones or senones, high-rate acoustic scoring is provided.
Lastly, the acoustic scoring unit 22 also includes an external memory address repository 36, a senone and ciphone score repository 38, a ciphone to senone mapping repository 40, and a senone scoring queue 42. The repositories 36-40 and the senone scoring queue 42 may be implemented in one or more internal memory devices of the acoustic scoring unit 22. For example, in one embodiment, the acoustic scoring unit 22 is implemented on a single IC, and the repositories 36-40 and the senone scoring queue 42 are implemented in one or more internal memory units included in the IC such as one or more Static Random Access Memory (SRAM) units. For each ciphone and each senone, an external memory device 44, such as a Dynamic Random Access Memory (DRAM) device, stores constants to be used for scoring that particular ciphone or senone. In one embodiment, the constants are Gaussian Mixture Model (GMM) constants, as discussed below.
For each ciphone in an acoustic model used by the acoustic scoring unit 22, the external memory address repository 36 stores one or more addresses in the external memory device 44 in which constants to be used for scoring that ciphone are stored. Likewise, for each senone in an acoustic model used by the acoustic scoring unit 22, the external memory address repository 36 stores one or more addresses in the external memory device 44 in which constants to be used for scoring that senone are stored. The senone and ciphone score repository 38 is used to store scores for each senone and ciphone. The ciphone to senone mapping repository 40 stores, for each ciphone, information identifying a number of senones associated with that ciphone. Preferably, the senones associated with each ciphone are unique such that no senone is associated with more than one ciphone. The senone scoring queue 42 is a queue of senones to be scored by the acoustic scoring logic 26.
The control logic 24 then passes the feature vector to the acoustic scoring logic 26. The acoustic scoring logic 26 and the top scored ciphone identification logic 28 operate to compute a score for each ciphone in the acoustic model used by the acoustic scoring unit 22 and identify a number (N) of highest scored ciphones (hereinafter “the N highest scored ciphones”), respectively (step 102). More specifically, for each ciphone in the acoustic model, the acoustic scoring logic 26 obtains the one or more addresses in the external memory device 44 storing the GMM constants to be used for scoring the ciphone from the external memory address repository 36. Using the one or more addresses for the ciphone, the acoustic scoring logic 26 obtains the GMM constants for the ciphone from the external memory device 44. The acoustic scoring logic 26 then computes the score for the ciphone based on the obtained constants and stores the score for the ciphone in the senone and ciphone score repository 38. Note that the details of the scoring process are discussed below.
As the acoustic scoring logic 26 scores the ciphones, the acoustic scoring logic 26 provides the scores for the ciphones to the top scored ciphone identification logic 28. Based on the received scores, the top scored ciphone identification logic 28 identifies the N highest scored ciphones. In the preferred embodiment, N=3 such that the N highest scored ciphones are the three highest scored ciphones. Once all ciphones have been scored and the N highest scored ciphones have been identified, the top scored ciphone identification logic 28 notifies the high score ciphone identification logic 30 of the N highest scored ciphones and the highest ciphone score. In addition, the top scored ciphone identification logic 28 instructs the queue insertion logic 34 to insert senones associated with the N highest scored ciphones into the senone scoring queue 42. In response, the queue insertion logic 34 obtains the senones associated with the N highest scored ciphones from the ciphone to senone mapping repository 40 and inserts those senones in the senone scoring queue 42.
Once the senones associated with the N highest scored ciphones have been inserted into the senone scoring queue 42, the control logic 24 is notified. At this point, processing splits into two parallel branches: branch A and branch B, which are performed simultaneously. In branch A, the acoustic scoring logic 26 computes scores for the senones associated with the N highest scored ciphones (step 104). More specifically, as discussed above, the senones associated with the N highest scored ciphones have already been inserted into the senone scoring queue 42 by the top scored ciphone identification logic 28 and the queue insertion logic 34. As such, the acoustic scoring logic 26 obtains the senones associated with the N highest scored ciphones from the senone scoring queue 42. For each of the senones associated with the N highest scored ciphones, the acoustic scoring logic 26 obtains the senone from the senone scoring queue 42 and then obtains the one or more addresses in the external memory device 44 storing the GMM constants to be used when scoring the senone from the external memory address repository 36. Using the one or more addresses for the senone, the acoustic scoring logic 26 obtains the GMM constants for the senone from the external memory device 44. The acoustic scoring logic 26 then computes the score for the senone based on the obtained constants and stores the score for the senone in the senone and ciphone score repository 38. Note that the details of the scoring process are discussed below. The acoustic scoring logic 26 continues this process to score all of the senones associated with the N highest scored ciphones.
At the same time that the acoustic scoring logic 26 is scoring the senones associated with the N highest scored ciphones in branch A, in branch B, the high scored ciphone identification logic 30 determines a threshold for high score ciphones (step 106). The threshold may be statically or dynamically defined. For example, the threshold may be dynamically defined based on scores computed for all ciphones in the current frame of sampled speech. Next, a counter (i) is set to zero, and a b_done value is set to “false” (step 108). The high score ciphone identification logic 30 then determines whether the counter (i) is less than a total number of ciphones in the acoustic model (step 110). If so, the high score ciphone identification logic 30 determines whether the score for ciphonei is greater than the threshold (step 112). If the score for ciphonei is greater than the threshold, the high score ciphone identification logic 30 determines whether ciphonei is one of the N highest scored ciphones (i.e., whether the score for ciphonei is one of the N highest ciphone scores) (step 114). If so, the counter (i) is incremented (step 116), and the process returns to step 110. However, if ciphonei is not one of the N highest scored ciphones, then the high score ciphone identification logic 30 and the queue insertion logic 34 add all senones associated with ciphonei to the senone scoring queue 42 (step 118). More specifically, the high score ciphone identification logic 30 instructs the queue insertion logic 34 to add senones associated with ciphonei to the senone scoring queue 42. In response, the queue insertion logic 34 obtains the senones associated with ciphonei from the ciphone to senone mapping repository 40 and adds those senones to the senone scoring queue 42. At this point, the counter (i) is incremented (step 116), and the process returns to step 110.
Returning to step 112, if the score for ciphonei is not greater than the threshold, the high score ciphone identification logic 30 notifies the low score ciphone scoring logic 32. The low score ciphone scoring logic 32 then assigns the score of ciphonei as the score for all senones associated with ciphonei (step 120). More specifically, using the ciphone to senone mapping repository 40, the low score ciphone scoring logic 32 identifies the senones associated with ciphonei. The low score ciphone scoring logic 32 then sets the scores for the associated senones in the senone and ciphone score repository 38 equal to the score for ciphonei. At this point, the counter (i) is incremented (step 116), and the process returns to step 110. Returning to step 110, once all ciphones have been processed, the high score ciphone identification logic 30 sets the b_done value to “true” (step 122), thereby indicating that branch B has completed processing for the current frame of sampled speech. At this point, the process for branch B ends.
Returning to step 104, once the acoustic scoring logic 26 has completed the scoring of the senones associated with the N highest scored ciphones in step 104, the acoustic scoring logic 26 then proceeds to score the senones inserted in the senone scoring queue 42 by the high score ciphone identification logic 30 and the queue insertion logic 34 in branch B until the senone scoring queue 42 is empty (step 124). Again, the senones in the senone scoring queue 42 are the senones associated with ciphones, other than the N highest scored ciphones, having scores computed in step 102 that are greater than the threshold determined in step 106. If or once the senone scoring queue 42 is empty, the acoustic scoring logic 26, or alternatively the control logic 24, determines whether branch B has completed processing (step 126). If not, the process returns to step 124 and is repeated until processing of branch B has completed and all senones inserted into the senone scoring queue 42 have been scored. Once branch B has completed processing and the senone scoring queue 42 is empty, the acoustic scoring logic 26 sends the senone scores from the senone and ciphone score repository 38 to a backend search stage or similar downstream stage in the speech recognition system (step 128). At this point, the process for branch A ends.
In the preferred embodiment, branch B normally completes processing before the acoustic scoring logic 26 has completed scoring the senones associated with the N highest scored ciphones in step 104. However, in the event that branch B does not complete processing before the acoustic scoring logic 26 has completed scoring the senones associated with the N highest scored ciphones in step 104, by monitoring the b_done value, the acoustic scoring unit 22 ensures that all senones associated with high scored ciphones are scored. Note that, in the preferred embodiment, the number (N) may be selected such that branch B completes processing before completion of the scoring of the senones associated with the N highest ciphones in step 104 in most, if not all, situations.
The acoustic scoring unit 22 of
where n(s) is the number of Gaussians in the mixture, ws,i is a weight of the i-th Gaussian for senone s, |Λs,i| is the determinant of covariance matrix Λs,i for the i-th Gaussian for senone s, σs,i,j2 is the variance for the j-th dimension of d-dimensional density for the i-th Gaussian for senone s, is the j-th element of d-dimensional feature vector X, and μs,i,j is the j-th element of a d-dimensional mean for the i-th Gaussian for senone s.
During the backend search stage, the repeated multiplication of the senone probabilities causes underflow errors. To prevent this, for each senone, the acoustic scoring unit 22 computes the log of the senone probability, which is referred to as the senone score or the score of the senone. Thus, the senone score for senone s may be computed as:
The base of the log computation, b, is generally a number marginally larger than 1 such as, for example, 1.0003.
When computing the senone score, instead of taking the log of the sum of the mixture component probabilities, it is advantageous to compute the individual log probabilities of each mixture component because the equations can be reworked to require fewer computations. The individual log probabilities of each mixture component may be computed as:
A log addition may then be performed to combine individual log probabilities of each mixture component to provide the senone score for senone s. Log addition is defined in L. R. Bahl, F. Jelinek, and R. L. Mercer, A maximum likelihood approach to continuous speech recognition, IEEE Journal of Pattern Analysis and Machine Intelligence, pages 179-190, 1983, which is hereby incorporated by reference for its teachings related to log addition.
The equation for computing the log of individual mixture component probabilities (EQ 3) can be rewritten such that storing a modified version of the weight and variance constants further reduces computations as shown in the following equation:
Instead of storing the weight, the log of the weight divided by the normalizing constant for the multidimensional Gaussian mixture component may be stored, which is defined as:
Also, instead of storing the variance, half of the reciprocal of the variance multiplied by the log of e may be stored as defined by:
Accordingly, based on Equations 4-6, the acoustic scoring logic 26 may score senones based on corresponding constants (αs,i, βs,i,j, and μs,i,j) stored in the external memory device 44. The same equations may also be used to score ciphones using corresponding constants for those ciphones.
Those skilled in the art will recognize improvements and modifications to the preferred embodiments of the present invention. All such improvements and modifications are considered within the scope of the concepts disclosed herein and the claims that follow.
This application claims the benefit of provisional patent application Ser. No. 61/008,727, filed Dec. 24, 2007, the disclosure of which is hereby incorporated herein by reference in its entirety.
This invention was made with government support under HR0011-07-3-0002 awarded by DARPA. The Government may have certain rights in this invention.
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Number | Date | Country | |
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61008727 | Dec 2007 | US |